Aspects of the disclosure relate to the efficient detection and classification of unauthorized transactional activity through the analysis of transactional data.
Banks, financial institutions, and other entities frequently employ algorithms to detect unauthorized, risky, or suspicious activity affecting client accounts. Sometimes, these algorithms monitor client accounts individually by analyzing data observations that are gathered when activity involving the account occurs. A sample of historical data observations can be referenced to determine typical user behavior, including account usage trends and patterns. Data generated to represent fraudulent transactions or transaction requests can also be gathered and analyzed to determine criminal behaviors, patterns, strategies, targets, or any other past information which may be used to better ascertain fraud likelihoods and risks, and classify newly occurring transactional activity.
When new transactional activity occurs, recent or real-time transactional data can be analyzed in search of information revealing that the activity is similar to other activity known to have been fraudulent or unauthorized. When such information is detected, appropriate security measures may be implemented to protect the account, as dictated by the level of risk ascertained from the information.